The arrival and service times were collected from 115 customers in Ferraz de Vasconcelos/SP city. It was performed in version 15.10 (2018) of the Arena software, with processor Intel core i3 CPU dual-core 3.07 GHz and 8GB of RAM. The results indicate that the bank agency should consider providing 9 to 11 operators to attend customers considering the arrival and service rate.
Keywords: Simulation; Arena Software; Queues; Banking Management
1.
INTRODUCTION
Government
and labor requirements have become customers more
bank-accounted as well as the growth of internet-linked services. The number of
online services available to Brazilian banks has been increasing since 2015,
where people are using smartphones and computers to connect to internet
banking. Nonetheless, bank agencies and branches have been decreasing due to
country technologic development, which caused an increase in the number of
customers and reduced the number of employees.
The
government imposed severe fines and sanctions on banks to avoid queues' time
overflow when the queue surpasses five minutes. Queuing system monitors were
adopted to attempt the solve these issues, but time overflow occur continually.
Therefore, the challenge is understanding two points. The first is a waste of
time-related to the service category since each category demands its time, and
the second it refers to customer perception. This perception consists of
customers' experiences with the system adopted (Ullah et al., 2014).
Recently, studies have focused on
queuing management using technologies to solve these issues (Mourõo et al., 2017; Kambli, Sinha, & Srinivas, 2020; Entringer, 2020;
Campos, Encarnação & Silva, 2019; Da Costa et al.,
2017; Clementino et al., 2018; Dos Santos, Cajuí & Da Silva, 2020; Pereira
Júnior, Da Silva & Moraes, 2020; Moraes & Silva, 2021). Some models have been proposed to identify the problem.
However,
all of them face difficulties identifying many variables on the model for all
the services categories and customers' experiences for a single queue.
According to Zheng (2011) and Mutingi, Mapfaira, Moakofi, Moeng, and Mbohwa (2015), the
importance of customer satisfaction and the time the customers wait before the
services taking into account business competition to service quality
evaluation. Cowdrey et al. (2018) studied different solutions for queuing
methods, such as First in First out (FIFO), Shortest Job First (SJF), Last in
First out (LIFO), and Longest Job First (LJF). They implemented a mobile
solution using a Field Programmable Gate Array (FPGA) to bank queuing
simulation. The results showed that LIFO disagree with the customers, and the
best outcomes were obtained with the SJF strategy at peak hours.
This
investigation presents a study about single queuing management through Discrete
Event Simulation (DES) by Arena Software to improve customer attendance
service, resulting in options that the bank can consider in designing
operators' service.
This
paper is organized as follows. Section 2 presents the theoretical references
about the queue and discrete simulation subjects, Section 3 details the
methodology used, and Section 4 shows the results of simulation using Arena
Software. Section 5 discusses the results. Finally, Section 5 summarizes the
results.
2.
BACKGROUND ON SIMULATION CONCEPTION
AND APPLICATION
To apply the simulation process
concerning many aspects, but the most important is to identify and minimize
process bottlenecks, and some techniques used on system modeling
are queuing theory and computer simulation (Prado, 2017). Koyama, Gonçalves, and Chin (2016) clarified that queuing is a part
of life routine, due to start when the resources are limited and the attending
demand is high. The Queuing Theory concerning to process of attending the
customer with agility and improve the company profit.
Moreover,
Taha (2016), fortify that queuing theory is not a study to system optimization,
but more than this is related to service performance indicators of the company.
Costa (2003) elucidated that there is different queuing compose for some kind
of attending, for instance, applied methods like FIFO – First in, First out or
LIFO – Last in, First Out, and random system that not show any standard defined
and is related the early attending considering priority criteria, in general,
by law.
According
to Andrade (2015), the most bottlenecks to be solved in a queuing study is how
to minimize the attending of customers congestion and reduce the accumulation
of people due to operational limited resource. The Author argued that queuing
congestion affect the company profit, so is it important to manage the timing
of the operation, as well as two fundamental variables that directly impact
system performance: 1) the numbers of customers arrivals per period; and 2) the
capacity to attending the quantity of customer in the system per period.
Exploring
the queuing theory, Arenales, Armentano,
Morabito and Yanasse (2015) highlight four queuing systems considering if
has a unique attending stage or multiple stages of completing customers
attending. For the three first models, there is the unique attending stage, and
the four models are the multiple stages.
2.1.
Simulation process and development of the concept
The
simulation process was built over the years, and it was improved by computers
during the II World War, as cited by Baladez (2009),
Oliveira, Loshi and Pires (2018), EDVAC, MARK I, and
ENIAC, the last one was used for ballistic artillery calculations in H Bomb
Manhattan project design. Up to 70 decades, the simulation building was too
expensive, due to the necessity to contract the specialized group with higher
acknowledgment.
The
simulation system model is a construction of graphics to present the
interaction among systems part, in other words, is an abstraction to reality,
as closer to real system behavior as possible (Chwif & Medina, 2014). As Prado (2017), concerning using the word simulation,
explained that there is much interpretation in this context to system modeling, but there is a consensus that when applying
simulation, a system or ideal process is being imitated.
Therefore,
Chwif and Medina (2014), presented concepts as a
system is a group of elements of a unique function, that are linked considering
some interaction and interdependence, to run some process. The entity is an
important object in the system, and an attribute is the characteristic of the
entity. Moreover, activity is described as some action consumption time to be
processed. The system state is a variable group important to describe the
system in a period. Besides, the event is defined as actions that could change
the status of the system.
The
simulation process is present in the household environment due to a personal
computer became a household appliance and it is used daily, so the simulation
process could be applying in many cases (Prado, 2017). However, considering a complex world and the creation of big data,
becoming easier to applying simulation, the technology aggregated value in this
process.
Therefore,
a simulator is software capable to produce and operate simulations of some
system behavior. Among software more common, it is
possible to highlight spacial, agricultural, textile,
flying, medical, shipping, wars, mechanical, transportation simulators, and so
on (Baladez, 2009). Nowadays, is common to use the
PROMODEL, HEXAGON, FLEXSIM, CAD, and ARENA software to construction small and
big simulation process to optimize time and profit. For instance, simulation
applied to optimize logistic operation especially transportation system as such
railroad, road, shipping, and airway.
De
Freitas Filho (2008)
mentioned that the system computer simulation translates the application of
mathematics techniques based on computer performance, to imitate process
function, system, and actual operation. Many authors present the simulation
concepts, however, is important to highlight what is not consider simulation,
in this aspect, Chwif and Medina (2014) clarified
that simulation is not a psychic process neither future predict model, is not a
mathematic model closed, as well as is not any descriptive tools and not
overlap an intelligent perception in process of decision making. The authors
continue to recommend that simulation should be not used after detection of
fails from other techniques that have been applied in the same way, simulation
should be avoid applied to solve specific problems.
To
clarity, Leal (2016), mentioned that simulation into the
computer system in the first step creates an ideal model, so data were obtained
based on queuing theory study. After this, it is possible to calculate the
statistics and identify the variables that represent a random behavior, and procedure with the distribution of
probability and building the sample to use in adhesion tests. To demonstrate
this, Freitas (2008) reinforced that the simulation process must follow some
standard to its construction, Figure 1.
Figure 1: Simulation process in blocks
Source: Adapted
from De Freitas Filho (2008)
On
the other hands, Law, Kelton and Kelton,
(2000), listed some common criteria on simulation model programming, as such:
·
To
create random numbers, in other words, observation from the distribution of
probability.
·
To
create random variables based on specific probability (for instance, Poisson or
exponential).
·
Advance
in simulated time should be applied on event discrete.
·
Inputting
data into the software.
·
Collect
data statistics from the block and report results to the direction.
·
Detection
of the error condition to a new adjustment
Taha
(2016) explained that queuing studies are probabilities into a stochastic
standard and because to be random it presents an indeterminate state due to
discrete event. So, this data into a simulation is translated as a behavior measure that reproduces the real system and how it
reacts. Moreover, every method shows
some advantages and disadvantages, in this way, Andrade (2015), argued that
there are many advantages in applying simulation for business, such as predict
results, reduction of decision errors, measure system performance, properly
resources apply. Moreover, Corrêa, Gianesi and Caon
(2001) cited that there
are also some disadvantages in using simulation associated with simulation
model size, due to demand time and complex technical process, as well as the
utilization of specialized hardware and expensive to data process.
3.
METHODS
Marconi and Lakatos (2017) explained that scientific research has the objective to guide and
support decisions adopted by researchers, following an action group and norms
that trying to reach the scope of the study considering reduce time, money, and
applying security to validate the finding. Based on Silva and Menezes (2005)
explanation, we consider this study as research applied due to it has the objective
to contribute to the solve real problem of the company used as a case study.
According
to Guedes and Araújo (2013), contextualized that in the banking sector, queuing
management is highlighted as an important method because it explores the limits
of the system to offer a great solution without extrapolating company
resources. Therefore, it is necessary to consider simulation models procedure
in three steps, first the conception of the system (clear objective and modeling conception), second conducting the computer
simulation model using specific software, and the last one is to obtaining
results after many simulation rotations (Chwif & Medina, 2006). The simulation models were performed
in version 15.10 (2018) of the Arena software, with processor Intel core i3 CPU
dual-core 3.07 GHz using 8GB of RAM. The data were collected in a bank agency in the city
of Ferraz de Vasconcelos/SP. The agency is open from
10h to 16h. The data is a sample of arrivals of 115 customers collected during
1h10 from 13h14 to 14h20, and the service time of operators from the same
period.
4.
RESULTS
The arrival times and service times were collected from
115 customers. The histogram of the data is presented in Figure 2. The
inter-arrival times were tested to identify which distribution would best fit
the data. We used the Input Analyzer of Arena to perform this analysis. The
best distribution fitted was Gama. The results of the tests can be seen in
Table 1.
Table 1: Results of the
test of inter-arrival times and service time
|
|
Inter-arrival times |
Service time |
|
Distribution |
Gama |
Triangular |
|
Expression |
-0.5+Gama (0.699, 2.23) |
TRIA (5.5, 9.2, 22.5) |
|
Squared error |
0.003264 |
0.007831 |
|
Chi-Square test |
1.58 |
8.01 |
|
p-value |
0.22 |
0.22 |
[a] Inter-arrival
times [b] Service
time
Figure 2: Histogram of the inter-arrival times and
service time
After the tests, the model was built on Arena as shown in
Figure 3.
Figure 3: Model in Arena
We performed 100 replications by changing the number of
operators to verify the change in the number of customers out of the system,
waiting time in the queue, queue length, and utilization. The results are shown
in Table 2.
Table 2: Results of simulation
|
|
Quantity of operators |
||||
|
|
9 |
10 |
11 |
12 |
13 |
|
Number of customers out |
254 |
279 |
303 |
318 |
323 |
|
Waiting time in queue (minutes) |
38.46 |
26.05 |
12.36 |
5.22 |
2.22 |
|
Queue length |
36 |
25 |
12 |
5 |
2 |
|
Utilization |
0.98 |
0.98 |
0.96 |
0.92 |
0.87 |
The
results varied in a different proportion due to the number of operators. In the
first scenario, with 9 operators, 254 customers are served, waiting on average
38 minutes in the queue. The queue length, in this case, is 36 customers long.
Also, there is high utilization (0.98) with 9 operators. On the other hand,
with 13 operators, 323 customers are served, the waiting time in queue is
reduced to an average of 2 minutes, the queue length is 2, and utilization
drops to 87%. Table 3 presents the relative variation in the parameters.
Table 3: Relative variation
|
Quantity
of operators |
9 |
10 |
11 |
12 |
13 |
Average change |
|
Number
of customers out |
- |
+9.84% |
+8.6% |
+5% |
+1.5% |
+6.23% |
|
Waiting
time in queue (minutes) |
- |
-32% |
-53% |
-58% |
-57% |
-50% |
|
Queue
length |
- |
-31% |
-52% |
-58% |
-60% |
-50.25% |
|
Utilization |
- |
0% |
-2% |
-4% |
-5% |
-2.75% |
As
can be seen in Table 3, the parameters that suffered the larger change were the
waiting time in the queue and the queue length, with a drop of 50% on average,
and the parameters that changed less were the utilization dropping only 2,75%
and the number of customers out rising 6,23%.
5.
DISCUSSION
In the present study, the simulation
showed that with 9 operators (banking service desk), the waiting time in queue
is on average more than 38 minutes. According to the law of the city of Ferraz de Vasconcelos, No. 2,682, from December 20, 2005,
the item I, on normal days, except the day before and after holidays, and on
paydays, a customer must wait in line for a maximum of 15 minutes.
To comply with the law of the city
of Ferraz de Vasconcelos, the bank branch must use 11
operators, increasing the number of operators by 22.22%, reducing the waiting
time to an average of 12.36 minutes, having 96% of operator utilization.
According to the simulation, 10
operators would still not be enough to comply with the law of the city, as the
waiting time would be 26 minutes, more than what the law determines. With 12
and 13 operators, there is a significant decrease in waiting time, maintaining
a good level of utilization grade by operators, but it could be an increase in
costs due to the need for more manpower.
The simulation and the Arena
software used by Fisher (2018) in his paper, confirm that the bank branch
complies with the city legislation. The Author, used the fixed simulation, the
same used in this paper, confirmed according to his data that the probability
of complying with the city legislation is 98.6%.
Paz, Teixeira & Alberti (2016),
focused the simulation using the Arena software went beyond, adapting the bank
branch to the local legislation, other alternatives were studied, such as the
qualification of employees, seeking a decrease in the normal service time by
20%. In this way, it could be possible to maintain the number of operators,
increasing the number of customers out from 212 to 224 and with the potential
to improve the utilization of the operators, because there was a reduction of
utilization from 95% to 83%.
The simulation proposed by Entringer (2020), increased the utilization of the
operators. The Author proposed the reduction of one operator, obtaining an
increase in the utilization from 50.07% to 71.10% and increasing from 1.47
minutes to 8.23 minutes on average in the waiting time in the queue, an
increase considered small by the Author.
The simulation was also used to
minimize queues. Pinto, Silva, Costa, & Macedo Lemos
(2018), proposed to increase the number of operators by 50%, from 2 to 3,
decreasing the average waiting time in the queue from 17.16 minutes to 1.48
minutes on average, and also decreasing in the utilization from 80.37% to
56.50%.
6.
CONCLUSION
The
simulation has proved to be an important tool for managing the queue and
decision-making at a bank branch. With the study, the bank branch can verify
the need to comply with the city law, increasing the number of operators from 9
to 11 operators, reducing the waiting time to 12 minutes, according to the law.
Increasing
two more operators can also increase costs, and to minimize it, financial
companies have been working for massive development using internet
transactions, and in automatization, their process and improvement programs can
be developed according to Paz et al (2016).
As a
suggestion for future studies, in addition to the improvement programs, more
data will be necessary, distributed over several times and days of the week and
in other situations, such as on days before and after holidays and paydays to
further study and improve the understanding of customer behavior at this
branch.
REFERENCES
Andrade, E. L. (2015). Introdução à Pesquisa Operacional: métodos e modelos para análise de decisões. 5. Ed. Rio de Janeiro: LTC.
Arenales, M.,
Armentano, V. A., Morabito, R., & Yanasse, H. H. (2015). Pesquisa
Operacional para cursos de engenharia (2ª ed.). Rio de Janeiro: Elsevier,
Campus, 723.
Baladez, F. (2009). O
passado, o presente e o futuro dos simuladores. Fasci-Tech
– Periódico Eletrônico da FATEC-São Caetano do Sul, São Caetano do Sul, 1(1),
29-40.
Campos,
C. C. S., da Encarnação, E., & da Silva, A. M. (2019). Simulação discreta
aplicada à distribuição da alimentação escolar: Estudo de caso em uma escola
pública. South
American Development Society Journal, 5(14), 290.
Chwif, L., & Medina, A. C. (2006). Uma análise crítica da Lei Municipal 13.948 ou ‘Lei das Filas’ sob a ótica da Pesquisa Operacional: conclusões derivadas de modelos de simulação de eventos discretos. Anais... XXVI ENEGEP–Encontro Nacional de Engenharia de Produção, Fortaleza, CE.
Chwif, L., & Medina, A. C. (2014). Modelagem e simulação de eventos discretos. 4a ed. Afonso C. Medina.
Clementino, M. R., da Silva, T. T., da Silva, A.
M., Tanaka, W. Y., & de Felice Zampini, E. (2018). Discrete simulation applied to a gas appliance
company. Independent Journal of Management & Production, 9(5),
699-715.
Corrêa, L. H., Gianesi, I.
G., & Caon, M. (2001). Uso de simulação para dimensionamento e gestão
de estoques de peças sobressalentes (4ª ed.). São Paulo: Editora Atlas. Acesso em 14/04/2020 de
https://www.researchgate.net/profile/Henrique-Correa-5/publication/237808567_Uso_de_simulacao_para_dimensionamento_e_gestao_de_estoques_de_pecas_sobressalentes/links/004635329a5e56bd56000000/Uso-de-simulacao-para-dimensionamento-e-gestao-de-estoques-de-pecas-sobressalentes.pdf.
Costa, L. C. (2003). Teoria das Filas. [Apostila da disciplina: Teoria das filas e simulação. Disponível em: <http://www.deinf.ufma.br/~mario/grad/filas/TeoriaFilas_Cajado.pdf>..
Cowdrey, K.
W., de Lange, J., Malekian, R., Wanneburg,
J., & Jose, A. C. (2018). Applying queueing theory for the optimization of
a banking model. Journal of Internet Technology, 19(2), 381-389.
de Freitas Filho, P. J. (2008). Introdução à modelagem e simulação de sistemas: com aplicações em Arena. (2ª ed.). Florianópolis: Visual Books.
Da Costa, L. S., Lúcio, W. D. S., Da Silva, A. M., & Ferreira, W. D. P. (2017). Discrete simulation applied to the production process of electronic components. Independent Journal of Management & Production, 8(5), 596-613.
Dos Santos, R. S. B., Cajui, R. M. F., & Da Silva, A. M. (2020).
Simulação a eventos discretos aplicada à gestão de filas em uma loja de
tecidos. South
American Development Society Journal, 6(17), 117.
Entringer, T. C. (2020). Simulation and
analysis of queues in banks: a case study of an agency in the southern State of
Rio de Janeiro. Independent Journal of Management & Production,
11(3), 892-907.
Guedes, D. B., & Araujo, A. C. (2013, Outubro). Gestão de filas: um estudo de caso em torno da qualidade dos serviços numa agência bancária da região metropolitana do Recife. Anais do XXXIII Encontro Nacional de Engenharia de Produção. Salvador, BA, Brasil. Acesso em 06/10/2020 de http://www.abepro.org.br/biblioteca/enegep2013_tn_sto_177_014_22639.pdf.
Kambli, A., Sinha, A. A., & Srinivas,
S. (2020). Improving
campus dining operations using capacity and queue management: A
simulation-based case study. Journal of Hospitality and Tourism Management,
43, 62-70.
Koyama, E. S., Gonçalves, I. B., & Chin, S. Y. (2016). Aplicação de teoria das filas em uma interseção de ruas e proposta de implantação semafórica. VI Congresso Brasileiro de Engenharia de Produção. Ponta Grossa. PR.
Law, A. M., Kelton, W. D., & Kelton, W. D.
(2000). Simulation modeling and analysis (Vol. 3). New York: McGraw-Hill.
Leal, B. G. (2016). Modelagem e Simulação Discreta.
Lei
n. 2.682, de 20 de dezembro de 2005. Dispõe sobre a obrigatoriedade das agências bancárias e demais
estabelecimentos de crédito de colocar a disposição
dos usuários, pessoas suficientes nos caixas para prestar atendimento digno e
eficaz a clientes. Recuperado em 16 de março de 2021, de
http://camaraferraz.lawsystem.com.br/admin/arquivos/img/Lei_2682_2005.pdf.
Marconi, M. D. A., & Lakatos, E. M. (2017). Fundamentos de metodologia científica. 8ª. ed.- São Paulo: Atlas.
Moraes, D. G., & Silva, A. M. (2021). Simulação discreta aplicada à gestão de filas no varejo. Exacta, 19(3).
Mourõo, R., Carvalho, R.,
Carvalho, R., & Ramos, G. N. (2017). Predicting waiting time overflow on bank teller
queues. In 2017 16th IEEE International Conference on Machine Learning and
Applications (ICMLA) (pp. 842-847). IEEE..
Mutingi, M., Mapfaira, H., Moakofi, N. P. K., Moeng, S. A.,
& Mbohwa, C. (2015). Simulation and analysis of a
bank queuing system. In 2015 International Conference on Industrial
Engineering and Operations Management (IEOM) (pp. 1-6). IEEE.
Oliveira,
G. L., Loschi, R. H., & Pires, M. C. (2018). Análise
de Regressão no software R: propriedades dos estimadores via método de Monte
Carlo, aplicações e exercícios [RELATORIO TÉCNICO, SERIE ENSINO RTE
01/2018]. Belo Horizonte: UFMG. Acesso em 28/11/2020 em
http://www.est.ufmg.br/portal/arquivos/rts/RTE_01_2018.pdf.
Paz,
F. J., Teixeira, R. F., & Alberti, R. A. (2016). Modelagem e simulação de
processos para melhoria operacional na agência do banco do brasil no município
de dom pedrito-rs. Revista da Jornada de
Pós-Graduação e Pesquisa-Congrega Urcamp,
829-845.
Pereira
Junior, J. V., Da Silva, A. M., & Moraes, D. G. (2020). Discrete simulation applied to queue
management in a supermarket. Independent
Journal of Management &
Production, 11(5), 1667-1684.
Pinto, A. C. P., da Silva, C. R., Costa, É. G., & Macedo Lemos, W.
(2018). A otimização da fila de uma agência do banco do brasil utilizando o
software. XV SEGET, Rio de Janeiro.
Prado, D. (2017). Teoria das Filas e da Simulação (Vol. 2). Falconi Editora.
Taha, H. A.(2016). Pesquisa operacional: uma
visão geral (5ªed.) São
Paulo: Pearson.
Ullah, A., Iqbal, K., Zhang, X. D., & Ayat, M. (2014, June).
Sub-optimization of bank queuing system by qualitative and quantitative
analysis. In 2014 11th International Conference on Service Systems and
Service Management (ICSSSM) (pp. 1-6). IEEE.
Zheng, H. (2011). Analysis and Restoration Strategies for Waiting for
Bank Services. Contemporary Logistics, (4), 83.